Datasets:

dignity045
/

Collective-Corpus

dataset_name: dignity045/Collective-Corpus
license: apache-2.0
language: multilingual
size_categories: 500B+ tokens
task_categories:
  - text-generation
  - fill-mask
  - text-classification
  - summarization
  - question-answering
pretty_name: Collective Corpus
tags:
  - pretraining
  - finetuning
  - large-language-model
  - code
  - math
  - instructions

🧠 Collective Corpus — Universal Pretraining + Finetuning Dataset (500B+ Tokens)

Collective-Corpus is a massive-scale, multi-domain dataset designed to train Transformer-based language models from scratch and finetune them across a wide variety of domains — all in one place.

📚 Dataset Scope

This dataset aims to cover the full LLM lifecycle, from raw pretraining to domain-specialized finetuning.

1. Pretraining Corpus

Large-scale, diverse multilingual text sources
Cleaned, deduplicated, and filtered for quality
Inspired by datasets like C4 and FineWeb

2. Domain-Specific Finetuning

Instruction Following & Dialogue — Chatbots, multi-turn conversations
Code — Python, JavaScript, Java, C++, and more
Math & Logical Reasoning
Specialized Fields — Research papers, technical documentation

📊 Scale

Total Tokens: 500B+
Estimated Text Samples: 700M+
Target Model Size: Suitable for training large models from scratch
Covers general-purpose and domain-specific training needs

🎯 Goals

Build a unified corpus for full-stack LLM development.
Enable open and reproducible large-scale language model research.
Support finetuning for high-impact domains like code, math, and dialogue.

🚧 Current Status

Model Pretraining: Currently training a Transformer model from scratch on the full 500B+ token dataset.
Public Release: Planned after model training completes.

🤝 Collaboration

We are actively seeking open-source collaborators to:

Contribute to dataset cleaning, filtering, and deduplication
Assist in large-scale model training and evaluation
Provide expertise for specialized domain corpora

We also offer free guidance on:

Dataset curation best practices
Efficient large-scale LLM training pipelines
Transformer architecture optimization

💼 Open for Collaboration

I’m actively looking to connect with researchers, engineers, and organizations passionate about dataset engineering, large-scale model training, and applied NLP.
Whether it’s open-source projects, research collaborations, or large-scale AI initiatives — let’s build something impactful together.

🔗 GitHub: Dhiraj309
🔗 LinkedIn: Dhiraj Patil

📅 Release Timeline

Stage	Status
Data Curation	🚧 In Progress
Model Pretraining	🚧 In Progress
Dataset Public Release	⏳ Post-training

📜 License

Released under the Apache License 2.0 — you are free to use, modify, and distribute this dataset in compliance with the full license text.